HW 03

if (!require("pacman")) 
  install.packages("pacman")

pacman::p_load(tidyverse,gghighlight,openintro,lubridate,palmerpenguins,glue,scales,countdown,ggthemes,gt,openintro,ggrepel,patchwork,quantreg,janitor,colorspace,broom,fs,here,dsbox,ggridges,gtable,ggimage,ggpubr,cowplot,png,sysfonts,showtext,grid)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

#Add Fonts as Needed
font_add("oldFont", "font/Wolfsmith.ttf")  
showtext_auto()

# set width of code output
options(width = 85)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7,        # 7" width
  fig.asp = 0.618,      # the golden ratio
  fig.retina = 3,       # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300             # higher dpi, sharper image
)

1 - Du Bois challenge.

# Read Data 
income <- read_csv("data/income.csv")

# Recode final class label for line break
income <- income |>
  mutate(
    Class = recode(Class, "$1000 AND OVER" = "1000\nAND OVER")
  )

# left table
label_table <- income |>
  mutate(
    Class = factor(Class, levels = rev(c(
      "$100-200", "$200-300", "$300-400", "$400-500",
      "$500-750", "$750-1000", "1000\nAND OVER"
    ))),
    row = as.numeric(Class)
  ) |>
  ggplot(aes(y = Class)) +
  geom_tile(aes(x = 1.5, width = 2, height = 1), fill = NA, color = "black") +
  geom_segment(
    data = tibble(y = seq(0.5, 7.5, 1)),
    aes(x = 0.9, xend = 2.9, y = y, yend = y),
    inherit.aes = FALSE,
    color = "gray7", linewidth = 0.2
  ) +
  geom_segment(
    data = tibble(x = c(0.9, 1.95, 2.9)),
    aes(x = x, xend = x, y = 0.5, yend = 7.5),
    inherit.aes = FALSE,
    color = "gray7", linewidth = 0.2
  ) +
  geom_text(aes(x = 1, label = Class), hjust = 0, size = 3.2, family = "oldFont") +
  geom_text(aes(x = 2, label = paste0("$", Average_Income)), hjust = 0, size = 3.2, family = "oldFont") +
  scale_y_discrete(limits = rev(c(
    "$100-200", "$200-300", "$300-400", "$400-500",
    "$500-750", "$750-1000", "1000\nAND OVER"
  ))) +
  xlim(0.9, 2.9) +
  labs(title = NULL, x = NULL, y = NULL) +
  theme_void() +
  theme(
    plot.margin = margin(5, 0, 5, 5),
    axis.text.y = element_blank(),
    plot.background = element_rect(fill = NA, color = NA),  
    panel.background = element_rect(fill = NA, color = NA)
  )

data <- income |>
  pivot_longer(cols = Rent:Other, names_to = "type", values_to = "measurement") |>
  mutate(
    type = factor(type, levels = c("Rent", "Food", "Clothes", "Tax", "Other")),
    Class = factor(Class, levels = rev(c(
      "$100-200", "$200-300", "$300-400", "$400-500",
      "$500-750", "$750-1000", "1000\nAND OVER"
    )))
  ) 
data$measurement[data$measurement == 0] <- 0.1

category <- c(
  Rent = "#000000",
  Food = "#8663A3",
  Clothes = "#E89A8D",
  Tax = "slategray3",
  Other = "#D6C8B0"
)

bar_plot <- ggplot(data, aes(x = Class, y = measurement, fill = type)) +
  geom_bar(stat = "identity", width = 0.8) +
  geom_text(
    aes(label = ifelse(measurement >= 1, paste0(measurement, "%"), "")),
    position = position_stack(vjust = 0.5),
    size = 3,
    color = "white"
  ) +
  scale_y_reverse() +
  scale_fill_manual(values = category) +
  coord_flip() +
  labs(
    title = "", 
  x = NULL,
  y = NULL,
  caption = "FOR FURTHER STATISTICS RAISE THIS FRAME."
  ) +
 
  theme(
    axis.text.y = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.major.x = element_blank(),
    legend.title = element_blank(),
    legend.position = "top",
    legend.direction = "horizontal",
    legend.justification = "center",
     plot.caption = element_text(
       family = "oldFont",
      hjust = 0.5,       
      size = 7,
      margin = margin(t = 10)
    ),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank()
  
  )


combined_plot <- (label_table + bar_plot + plot_layout(widths = c(1.3, 3)))

final_plot <- ggbackground(combined_plot, "images/parchment_paper-1074131_1920.png")
print(final_plot)

2 - COVID survey - interpret

This plot offers a comprehensive look at how medical and nursing students from various backgrounds responded to statements about the COVID-19 vaccine, using a Likert scale where lower scores signify greater agreement with positive vaccine sentiments

Asian students expressed more concern about safety/side effects of Vaccine

Seems Asian students have a higher mean score than most other groups. This reflects the dynamic Cultural aspects & maybe historical experiences with medical procedures.

Think it is little disappointing compared to, how they had high compliance with public health measures otherwise.

Black/African students report higher agreement recommending vaccine

Despite broader societal data showing lower vaccine confidence among Black Americans (due to historic medical mistreatment and systemic inequities), this subgroup shows Strong agreement with recommedning vaccine.

Only reason I believe this is as thy are healthcare students, who may have more access to scientific information.

Medical students tend to be more confident than nursing students

Medical students show slightly lower (more confident) scores in: The scientific vetting process & on trusting the information received

Medical students may have had more exposure to the bio-medical research or vaccine development process during training, resulting in higher trust in science. Agree with this assessment.

3 - COVID survey - reconstruct

4 - COVID survey - re-reconstruct

5 - COVID survey - another view